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Investigating Community Implementation of the GoodRelations Ontology
 

Investigating Community Implementation of the GoodRelations Ontology

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GoodRelations is one of the most

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    Investigating Community Implementation of the GoodRelations Ontology Investigating Community Implementation of the GoodRelations Ontology Presentation Transcript

    • Open eBusiness Ontology Usage: Investigating Community Implementation of Good Relations Jamshaid Ashraf, Richard Cyganiak , Sean O’Riain, Maja Hadzic >> LDOW2011, March 29, 2011, Hyderabad, India
    • Building the Web of Linked Data
      • Think
      • Design
      • Prototype
      • Deploy
      • Evangelize
      • Adoption!
    • Building the Web of Linked Data
      • Think
      • Design
      • Prototype
      • Deploy
      • Evangelize
      • Adoption!
      This works!
    • Building the Web of Linked Data
      • Think
      • Design
      • Prototype
      • Deploy
      • Evangelize
      • Adoption!
      • Measure and analyze
      • Learn from it to influence future thinking and design
    •  
    • Selling something?
    •  
    •  
    • Jay Myers, BestBuy.com “ The RDFa data is ‘also great for machines,’ said Myers, which has resulted in ‘a definite up tick in the amount of search traffic to these pages.’ At last week's SemTech conference, Myers said that it had resulted in a 30% increase in search traffic . He noted that Best Buy hadn't expected to see an SEO benefit, but it's been a boon to them since the company is ‘very reliant on search engines’ for product discovery and store locations.”
    • But what exactly is being adopted?
    • Objectives
      • Who is publishing GoodRelations data?
      • What’s in the data?
      • What can you do with it?
      • Can we reason over GoodRelations data?
      • How to do this kind of empirical analysis?
    • Objectives
      • Who is publishing GoodRelations data?
      • What’s in the data?
      • What can you do with it?
      • Can we reason over GoodRelations data?
      • How to do this kind of empirical analysis?
      WORK IN PROGRESS
    • Dataset
      • Collected via Sindice, Watson, Google, Linked Open Commerce, GR wiki
      • 105 web sources (web sites)
      • Published Company and/or product/service Offering
      GoodRelations Dataset (GRDS)
    • Analysis
    • Who publishes and how?
      • Large retailers: Best Buy, Overstock.com, O’Reilly, Suitcase.com
      • E-commerce CMS packages with GoodRelations support
      • Third-party RDFizers (Virtuoso Sponger, RDF Book Mashup)
      • 90% RDFa
    • Who publishes and how?
    • Vocabulary co-use Prefixes and Namespaces used in GRDS Prefix Namespace URI Data sources (%) gr http://purl.org/goodrelations/v1# 100 vCard http://www.w3.org/2006/vcard/ns# http://www.w3.org/2001/vcard-rdf/3.0 (deprecated namespace) 88.57 dcterm dc http://purl.org/dc/terms/ http://purl.org/dc/elements/1.1/ 34.29 foaf http://xmlns.com/foaf/0.1/ 25.71 commerce media use currency http://search.yahoo.com/searchmonkey/commerce/ http://search.yahoo.com/searchmonkey/media/ http://search.yahoo.com/searchmonkey-datatype/use/ http://search.yahoo.com/searchmonkey-datatype/currency/ 14.29 v http://rdf.data-vocabulary.org 3.81 og http://opengraphprotocol.org/schema/ 0.95 rev http://purl.org/stuff/rev# 0.95 frbr http://vocab.org/frbr/core# 0.95 geo http://www.w3.org/2003/01/geo/wgs84_pos# 0.95
    • Concept Coverage
      • Offering
      • BusinessEntity
    • GRDS data coverage Findings…
    • Use Cases Finding a Company (Business Entity)
      • Find a company with a specific name
      • Find a company in a particular location
      • Find a company in a particular line of business (or service)
      Use of location related attributes in GRDS Object properties and their usage with : Offering in GRDS Map build using GRDS based on vCard country-name and locality RDF Terms Data sources (%) RDF Terms Data sources (%) :BusinessEntity 100 vCard:Address 99.5 :legalName 93.34 vCard:country-name 99.5 :hasISICv4 0.95 vCard:locality 99.5 :hasNAICS 0.0 vCard:street-address 85.3 :hasDUNS 0.0 vCard:postal-code 85.3 :hasGlobalLocationNumber 0.0
    • Use Cases Finding an Offer (:Offering) Find offering of a specific price range Find offering of a specific product and the available quantity Find delivery, warranty and payment charges of particular offering : Offering data properties object properties RDF Terms Data sources (%) RDF Terms Data sources (%) :Offering 100 :validFrom 82.86 rdfs:comment 77.14 :validThrough 82.86 rdfs:label 8.57 :eligibleRegions 82.86 v:name 0.95 :hasStockKeepingUnit 2.86 v:description 0.95 :hasEAN_UCC-13 1.90 v:price 0.95 :name 0.95 v:category 0.95 :description 0.95 dc:title 0.95 :availabilityStarts 0.95 dc:contributor 0.95 :hasGTIN-14 0.0 dc:date 0.95 :hasMPN 0.0 dc:description 0.95 :condition 0.0 dc:type 0.95 :serialNumber 0.0 dc:duration 0.95 :availabilityEnds 0.0 RDF Term % of Data sources RDF Term % of Data sources :Offering 100 :eligibleCustomerTypes 80.95 :hasWarrantyPromise 2.86 :hasBusinessFunction 77.14 :hasInventoryLevel 0 :availableAtOrFrom 70.48 :advanceBookingRequirement 0 :acceptedPaymentMethods 60.95 :deliveryLeadTime 0 :includesObject 56.19 :eligibleDuration 0 :availableDeliveryMethods 47.62 :eligibleQuantity 0 :hasPriceSpecification 30.48 :eligibleTransactionVolume 0 :includes 3.8 :hasEligibleQuantity 0
    • Use Cases Finding a specific product (Product Find-ability)
      • Find a particular product (e.g. TV or Shoes)
      • Find a product with specific requirement (e.g. TV set of 24 inches, HD resolution)
      97% only textual description of product
    • Reasoning
      • Quantitative value data properties
      • Currency value data properties
      Axioms in GRO and applicable rule sets
      • Few OWL-inconsistencies
      • Subclass inference ✓
      • Subproperty inference, inverse, transitive, symmetric ✗
      Axioms Count Applicable Rule sets Class SubClassOf 13 RDFS DisjointClasses 91 OWL2RL Object Property SubPropertyOf 4 RDFS InverseOf 6 pD*, OWL2RL TransitiveProperty 7 pD*, OWL2RL SymmetricProperty 2 pD*, OWL2RL Data Property SubPropertyOf 13 RDFS
    • What did we learn?
      • Small part of the ontology widely used
      • Main limitation: lack of detailed product model data
    • What did we learn?
      • Adoption driven by a major consumer who can create incentives
      • Key adopters: major retailers, e-shop software packages; not small individual shops
    • Future work
      • Make dataset available
      • Instance data quality, consistency
      • Recommendations to publishers and vocabulary designers
      • Towards a framework for vocabulary/ontology usage analysis
    • Thanks! Questions………